CAJ | 학술논문

概率假设密度（probability hypothesis density，PHD）滤波的序贯蒙特卡罗实现算法性能高度依赖于先验目标生成强度函数和粒子重要性采样（importance sampling，IS）函数。针对上述问题，提出一种改进算法。首先，引入量测驱动机制，提出一种量测分类方法获取潜在的新生目标量测集合，并以此为基础进行新生目标粒子采样，提高了算法的有效性。其次，为了提高存活目标粒子分布的准确性，结合门技术和无迹信息滤波将当前量测信息融入到 IS 函数设计中。计算机仿真实验表明，所提算法具有更稳健的多目标跟踪能力和杂波适应性。
개솔가설밀도（probability hypothesis density，PHD）려파적서관몽특잡라실현산법성능고도의뢰우선험목표생성강도함수화입자중요성채양（importance sampling，IS）함수。침대상술문제，제출일충개진산법。수선，인입량측구동궤제，제출일충량측분류방법획취잠재적신생목표량측집합，병이차위기출진행신생목표입자채양，제고료산법적유효성。기차，위료제고존활목표입자분포적준학성，결합문기술화무적신식려파장당전량측신식융입도 IS 함수설계중。계산궤방진실험표명，소제산법구유경은건적다목표근종능력화잡파괄응성。
The performance of probability hypothesis density (PHD)filter depends heavily on the priori of birth target intensity and the selection of importance sampling (IS)function when the sequential Monte Carlo method is used to implement it.To solve these problems,an improved algorithm is proposed.Firstly,a meas-urement-driven mechanism is introduced to classify the measurements to get the birth measurements which are used for exploring newborn targets.Secondly,the unscented information filtering is used to incorporate the cur-rent measurements information into the IS function,combined with the gate technique to choose the measure-ments matching with the persistent targets.The results of computer simulation indicate that the proposed algo-rithm outperforms similar algorithms in its ability to operate in clutter,and can initiate and estimate targets more accurately.